On a related note: Would love to see cyclomatic complexity as a supporting metric for readability. I increasingly find AI-adjacent and AI-written code suffering from this.
On the one hand, I absolutely agree that we should try to measure and reduce verbosity that LLMs are slopping into our codebases.
On the other hand, the fact that Astra is "winning" this bench is not great signal for me, since imo Astra produces basically unreadable code. Astra plays code golf. My own revealed preference is that I'd rather have the excessive comments and tests of Claude rather than the inscrutability of Astra.
I keep going in between the two and I'm currently in an Astra season, deeply disliking the tone and modes of Claude.
I wonder if the two will diverge enough that this becomes the tab-vs-spaces/vim-vs-emacs of the LLM era and they'll manage to keep users moving over at some point.
Nobody is going to care about how much code an AI system generates in the very near future, some even now, once everyone understands the scale at which a vector space truly operates at. Complexity is not much of an issue for an AI system.
Why even benchmark anything? I have faith that the AI will just get better over time anyway. I think to even attempt to measure any usability is an insult to the infinite exponential curve of improvement.
LLM systems start to Frankenstein things together at a very modest amount of code. The lack of abstractions cause huge and glaring problems for anything that needs to run predictably.
Abstraction is an issue for every AI coding agent. They are painfully useful, but need constant badgering in the right direction.
After 22,948 commits, 13,720 bugs and -2,899,391 lines eliminated, I've seen this too but a properly designed AI pipeline's ability to optimize and course correct with a little badgering supersedes any Frankenstein-like outcomes.
At this point, I really want all AI benchmarks to use the best coding/writing model that normal people can run locally.
For now that seems to be Qwen 3.8 27B that requires ~28GB of RAM (Q4) which is pretty doable for anyone with a beefy GPU or slightly-above-average mac.
I want to know how close open weights (open models) are to all these leading commercial models. Yes, yes, local models aren't as good, but that distance is closing as local models are already better than what we had just over a year ago in many cases.
Same with Text-to-Image generation (Flux 2, Z-Image-Turbo, etc..) or text-to-speech (Parakeet, Whisper, etc..).
Anything that can increase code quality would help.
On the other hand, the fact that Astra is "winning" this bench is not great signal for me, since imo Astra produces basically unreadable code. Astra plays code golf. My own revealed preference is that I'd rather have the excessive comments and tests of Claude rather than the inscrutability of Astra.
Astra codes like it has to pay by the keystroke (although in a way, it indeed does)
I wonder if the two will diverge enough that this becomes the tab-vs-spaces/vim-vs-emacs of the LLM era and they'll manage to keep users moving over at some point.
Abstraction is an issue for every AI coding agent. They are painfully useful, but need constant badgering in the right direction.
For now that seems to be Qwen 3.8 27B that requires ~28GB of RAM (Q4) which is pretty doable for anyone with a beefy GPU or slightly-above-average mac.
I want to know how close open weights (open models) are to all these leading commercial models. Yes, yes, local models aren't as good, but that distance is closing as local models are already better than what we had just over a year ago in many cases.
Same with Text-to-Image generation (Flux 2, Z-Image-Turbo, etc..) or text-to-speech (Parakeet, Whisper, etc..).
https://artificialanalysis.ai/?intelligence=agentic-index